Best Kafka UI tools for Confluent Cloud

Tools compared

Rank	Tool	Total (out of 100)	Governance beyond Confluent RBAC	Out of the data path	Connect, registry and ksqlDB	API keys, OAuth and mTLS	On-prem and cloud together	Inspecting topic data	Cost a year, one cluster (modelled)
1	Kpow	89	RBAC, staged approvals, temporary access, masking, tenants, audit	One container, no external database, not a proxy	Managed Connect, Schema Registry with data rules, hosted ksqlDB	All three documented	Confluent Cloud, Platform, MSK and Apache Kafka from one deployment	kJQ search, data inspect, rule failures	$7,380
2	Confluent Cloud Console	75	Per-user RBAC and audit logs; no masking or time-boxed access	Nothing to deploy	Every Confluent service, including Flink	Your own Confluent user account	Confluent Cloud, plus registered Confluent Platform clusters through Unified Stream Manager	Message browser; filters the results on screen	$0, included
3	AKHQ	67	Group roles, no masking	One container	Schema Registry, Connect and ksqlDB; ksqlDB needs TLS and ALPN flags its docs omit	API key, Confluent Cloud example	Named connections	Message browsing	$8,640
4	Kafbat UI	66	RBAC, global masking, opt-in audit	One container	Schema Registry, Connect and ksqlDB; no managed connectors	Broke on Confluent Cloud in v1.4.x and v1.5.0	Many providers, Confluent Cloud regressions	Message browsing	$8,640
5	Lenses	63	SSO and RBAC from Team tier, audit	HQ on PostgreSQL plus an agent per cluster	Schema Registry and Connect per environment	API key, Confluent Cloud guide	One agent per cluster, any provider	SQL over topics	$2,880 plus a quoted licence
6	Redpanda Console	63	Behind a paid Enterprise licence	One container	Schema Registry and Connect by URL; no ksqlDB	API key as SASL/PLAIN	One console per cluster	Message browsing	$8,640 plus an unpublished licence for RBAC
7	Conduktor	70	Strong; data-level controls through Gateway	Console on PostgreSQL; Gateway, a proxy, for data-level controls	Schema Registry, Connect and Confluent RBAC role bindings	API key	Confluent Cloud, Aiven, MSK and Cloudera	Message browsing	$32,880; $122,880 with Gateway Core and Protect

The tools, ranked for Confluent Cloud

What teams on Confluent Cloud need

Confluent Cloud runs the brokers, the managed connectors, Schema Registry and ksqlDB for you, and its own console covers each of them. This page is about what a tool adds on top of the service, for teams where many engineers share the same clusters. For the broader field of Kafka tools on any distribution, the best Kafka management tools are listed under related reading.

Confluent Control Center is the UI most people associate with Confluent. Its current documentation covers Confluent Platform, the self-managed distribution. Control Center (Legacy) can be connected to Confluent Cloud to monitor data streams through client interceptors, with an additional subscription unless the organisation has committed usage, so on Confluent Cloud the management UI is the Confluent Cloud Console. The difference between the two, and what a third-party tool adds to Control Center, is covered in Kpow vs Confluent Control Center.

API keys, OAuth and mTLS. A third-party tool has to connect with the authentication the cluster already enforces. Most teams use a cluster API key over SASL/PLAIN and TLS. Organisations that federate workloads through their identity provider use OAuth with an identity pool, which needs the logical cluster ID and identity pool ID passed as token extensions, and supported cluster types accept mTLS with a client certificate signed by a CA uploaded to the organisation. Every tool on this page can be made to use an API key; Kpow documents all three methods with Confluent’s specific settings, and the Kafbat UI review records Confluent Cloud clusters stuck initialising on its v1.4.x and v1.5.0 releases.

Which account owns the credential matters as much as the mechanism. Confluent’s API key documentation states that permissions are not associated with an API key but with the user account or service account that owns it, that a key owned by a user account is deleted when that account is deleted, and that group mapping permissions are not granted to a key owned by an SSO user account. It recommends service account keys for production and rotation every 90 days. A shared tool should therefore connect as a service account of its own with role bindings sized for the tool, because a key created under an engineer’s account carries that engineer’s permissions and stops working when the engineer leaves. A tool that covers the whole service also holds several credentials, since Confluent scopes keys by resource: one for the Kafka cluster, one for Schema Registry, one for the ksqlDB cluster, and a Cloud API key for the Metrics API and the Connect API. Monitoring products need the same extra key, and Grafana’s Confluent Cloud integration begins by creating a Cloud API key, so the rotation schedule has to cover every key the tool holds.

OAuth through an identity pool replaces API keys with short-lived tokens issued by the organisation’s own identity provider, and it rests on standard Apache Kafka. The OAUTHBEARER login handler that Kafka gained in KIP-768 passes any extension_ value in the JAAS configuration to the broker as a SASL extension, which is how Confluent’s logical cluster and identity pool IDs travel. A tool built on the standard Java client therefore needs two extra settings and no Confluent-specific code, and a tool that ships its own Kafka client has to implement the extensions before it can use an identity pool at all.

Connect, registry and ksqlDB. The tool also has to reach the services around the cluster. Fully managed connectors are run through the Confluent Cloud Connect API, which takes a cloud API key rather than the Kafka Connect REST endpoint of a self-hosted worker. Schema Registry has its own API key, and its schemas can carry data contract rules. ksqlDB is a separate hosted cluster with an API key of its own, and disk usage is not returned by the Kafka AdminClient at all, only by the Metrics API. Confluent now recommends its Flink service for new stream processing on Confluent Cloud and keeps ksqlDB fully supported for existing applications, so ksqlDB support matters most to teams with ksqlDB applications already running, and none of the third-party tools here manages Confluent’s Flink. Kpow reads and manages managed connectors, Schema Registry, hosted ksqlDB and the Metrics API; the open-source UIs connect to Schema Registry and Kafka Connect by URL, and AKHQ and Kafbat UI also connect to ksqlDB, AKHQ only with TLS and ALPN flags its documentation does not list. Connector tooling is covered in Kafka Connect monitoring tools.

Managed connectors differ from self-managed Kafka Connect in how they are operated as well as in which API reaches them. The open-source Kafka Connect REST API exposes every task, with a status for each and an endpoint that restarts an individual task, and a self-managed worker writes its own log files. On Confluent Cloud the workers belong to Confluent, the connector is managed through the Confluent Cloud Connect API, and its logs arrive as connector events, which Confluent’s documentation says the console keeps for three days by default. A tool that manages both kinds has to read each in its own terms, and a team that keeps some connectors on its own workers beside the managed ones needs both in one view.

The other services leave traces in the cluster that a tool should be able to explain. ksqlDB is built on Kafka Streams and compiles each persistent query into a Kafka Streams topology, so every query appears in the cluster as a consumer group with internal repartition and changelog topics that nobody created by hand, and Confluent’s audit log reference notes that kafka.DeleteRecords events are commonly seen on ksqlDB internal topics. An engineer who finds an unfamiliar consumer group on a Confluent Cloud cluster needs the group, its lag and the query behind it in one place. Data contract rules are written in open expression languages, CEL and JSONata, and Confluent’s documentation says the client runs them during serialisation or deserialisation, so a record that breaks a rule is caught by whichever client holds the rule executor and never shows up as a broker metric. Seeing which records failed takes a tool that reads the topic with the registry’s rules loaded. Schema Registry deletion has two levels as well: Confluent’s documentation describes a soft delete, after which the schema ID still resolves, and a hard delete that removes all metadata including schema IDs, so a UI should keep the two actions clearly apart.

Governance beyond Confluent RBAC. Confluent RBAC binds roles to user accounts and service accounts, and Confluent’s audit logs record each permission check with the principal that made it; Confluent’s own example of an audit record is a service account connecting with an API key. When twenty engineers share one tool, that service account is the tool’s, so Confluent sees the tool, not the engineer. Engineers who work in the Confluent Cloud Console are recorded under their own accounts, but the console has no masking, no approval step and no access that expires. Per-person roles, masking of sensitive fields, an approval step in front of offset resets and topic deletion, production access that expires, and an audit trail that names the person have to come from the tool.

The way Confluent grants permissions is what makes the tool’s own roles necessary. Because permissions attach to the account that owns an API key and not to the key, every engineer working through a shared tool acts with the whole of the tool’s service account. Narrowing that per person inside Confluent would mean one account and one key for each engineer, which is the console’s model and not a shared tool’s. The tool’s roles are therefore the only place where one engineer can be limited to reading two topics while another may reset offsets, the least privilege a security review looks for, and a tool with no roles of its own gives each person who opens it everything its service account can do. Engineers who do work in the console sign in under their own accounts, and Microsoft documents single sign-on to Confluent Cloud with Microsoft Entra ID for organisations that buy the service through Azure.

Confluent’s audit logs are built around the service’s own principals, and three details decide what a team can get from them. They are written to an independent, read-only audit log cluster that keeps seven days by default and needs an API key of its own to consume, and Confluent’s documentation says they cannot be consumed with a fully managed sink connector, so keeping them longer means running a consumer, a self-managed connector or a cluster link. Each record identifies the principal by ID, in Confluent’s own example User:306343 with the resource ID u-yw9507, so tying a record to a team is a lookup against the organisation’s accounts. And every record, whether an authentication, a permission check or an administrative request such as kafka.DeleteTopics or kafka.OffsetDelete, is attributed to the connecting principal, which for work done through a shared tool is the tool’s service account, so the engineer never appears in it. PCI DSS asks for audit logs that capture each individual user’s access to cardholder data, and the wider question of which person read a topic that holds customer data is one the tool that person signed in to has to answer.

Confluent role bindings are standing grants that stay until someone removes them. The cloud providers treat time-limited elevation as a separate control: Google Cloud’s Privileged Access Manager describes just-in-time temporary privilege elevation with a record of who had access to what and when, and approvals that can be automated from an ITSM ticket. TD applies that pattern to Kafka across its Confluent Cloud and on-premises clusters, as the customer card below sets out.

Out of the data path. One requirement cuts across all of these: where the tool runs. A tool that runs as a container in your own network and connects to Confluent Cloud the way any Kafka client does, over the same public endpoints or private networking as your applications, adds nothing to the path your producers and consumers take. A tool whose controls are enforced by a proxy that every client connects through becomes part of that path, and it has to be sized, kept available and kept in step with every client upgrade. Kpow is one container with no external database, installed in your own environment and out of the data path. Conduktor Console also connects to Confluent Cloud directly, with a PostgreSQL database of its own; Conduktor’s data-level controls, such as encryption, masking of the data itself and virtual clusters, run in Conduktor Gateway, a Kafka proxy that client applications connect through.

On a managed service a proxy is also the one component in the path that the provider does not run. Confluent operates the brokers, and a Kafka proxy in front of them is deployed, scaled and kept available by the customer, the costs that Kai Waehner’s review of Kafka proxies lists as an extra network hop, a high-availability requirement and operational overhead, set against what a proxy can enforce for every client without application changes.

Private networking makes the tool’s location matter more. Confluent’s documentation states that on a cluster with private networking the endpoints behind topic management and ksqlDB query management are not publicly accessible, so the Confluent Cloud Console on a workstation outside the private network cannot show topic data. The options it gives are a resource metadata setting that shows topics and their metrics without messages, or a connection into the network through a proxy, a reverse SSH tunnel or local DNS changes, components Confluent places outside its support scope. That is the intended effect of private connectivity, which AWS PrivateLink describes as reaching a service from private subnets without an internet gateway or public IP address. A tool that runs as a container in the VPC or VNet that already holds the private endpoints reaches the cluster the way the applications do, and engineers reach the tool through the company’s own sign-in, so nothing further has to be opened.

Keeping no database has a cost of its own on Confluent Cloud. Kpow holds its snapshots, metrics and audit log in internal topics on the cluster it manages, which its system requirements put at up to 10 GB of replicated disk at the default one-week retention, and Confluent bills ingress, egress and storage as cluster usage, so those topics and the records Kpow reads are part of the Confluent Cloud bill in the same way the message browser’s reads are. Kpow’s availability also follows that cluster’s, and its system requirements ask for it to run close to the Kafka resources it observes.

On-prem and cloud together. Confluent Cloud is rarely the only Kafka a large organisation runs, and work that starts in a provider’s own interface does not have to stay there. NORD/LB, which runs Confluent’s self-managed platform, records in its case study that it moved the whole of its ksqlDB query development into Kpow after reaching a 3,000-line query limit in its previous interface. A provider’s console is also what engineers learn, so when daily work runs through it a change of provider retrains every engineer as well as moving the data. Kai Waehner’s data streaming landscape for the third quarter of 2026 frames the same year, in which IBM closed its acquisition of Confluent, as a question of who controls an organisation’s streams, and the Migrating to open source Kafka session reports that what teams miss after leaving a bundled platform is the UI and the view of their data, because not every user is comfortable on the command line.

Inspecting topic data. On Confluent Cloud, field-level encryption decides which tools can show a record at all. Confluent’s documentation for client-side field level encryption says that encryption and decryption execute on the client, and that when the key encryption key is not shared with Confluent the data cannot be decrypted inside Confluent Cloud for stream processing. TD could not send card data to an internet-facing Kafka service until it encrypted every message itself, as the TD talk describes, so a tool without the bank’s library sees only ciphertext, and the customer card below sets out how the bank reads that data. Custom SerDes run inside the tool’s JVM and share its library versions, which has a maintenance cost: when Confluent’s Protobuf serializer moved from Protobuf 3.25.5 to 4.31.1, SerDes containing generated Protobuf classes had to be regenerated, as the Kpow note on that upgrade explains.

No tool here leads on every criterion. The Confluent Cloud Console needs nothing deployed and covers every Confluent service, including Flink and Stream Lineage, which Kpow does not. Lenses has the stronger query model with SQL over topics. Kpow governs people working through Kpow, so applications keep their own API keys and service accounts, and Confluent RBAC or Kafka ACLs stay the control for them. For new stream processing, Confluent recommends its Flink service, which Kpow does not manage.

Who runs Kpow on Confluent Cloud

Two Kpow customers with Confluent Cloud in their public record are named here. TD Bank has described in a public talk how it uses Kpow across Confluent Cloud and on-premises clusters, and its card is tagged with the rubric criteria that evidence speaks to. Allianz Direct has not described its Kpow use in public, so its card carries what a December 2022 press release said about its Confluent Cloud data stack. The TD talk also shows what a shared console changes for the platform team. TD’s team created every artefact by hand until the load forced it to build self-service, and onboarding a team to Kpow is now a change inside the bank: the team is added to an Active Directory group through ServiceNow, and the platform team sets up its tenant and access policy. Collecting consumer lag with the Kafka CLI took 20 minutes or more for each cluster, and the same job through Kpow runs within two minutes, so TD collects lag every five minutes and client teams raise their own tickets from it.

How a team runs Confluent Cloud with Kpow

Installing it in your own network. A team runs Kpow as a Docker container, on Kubernetes with the Helm charts, or as a JAR on a virtual machine, in the cloud account and network its applications already use to reach Confluent Cloud. The full walkthrough is Set Up Kpow with Confluent Cloud. On a cluster with private networking that network is the VPC or VNet that already holds the private endpoints, so Kpow needs no proxy or tunnel of the kind the Confluent Cloud Console needs from outside. Kpow connects with the same configuration as a Kafka producer or consumer, so applications keep talking to the brokers directly and adopting or removing it changes nothing about how they connect. It is priced per cluster, which suits a service that sizes clusters in eCKUs and CKUs rather than in brokers.

Connecting to the cluster. Kpow connects with a cluster API key over SASL/PLAIN, with OAuth through an identity pool, or with mTLS on a dedicated cluster, and its Confluent Cloud cluster documentation gives the settings for each. The identity Kpow connects as needs Confluent roles, either CloudClusterAdmin or, for least privilege, a mix of DeveloperRead, DeveloperWrite and DeveloperManage, or Kafka ACLs, which Kpow can then view and manage itself. Several clusters behind one bootstrap address are told apart with a CLUSTER_ID per cluster. Following Confluent’s guidance on API keys, that identity is best a service account created for Kpow, and the Cloud API key Kpow uses for the Metrics API can belong to a service account that holds only Confluent’s MetricsViewer role, which Confluent describes as giving a service account access to the Metrics API.

Signing people in. Engineers sign in to Kpow through SAML, OpenID Connect or LDAP, so access follows the company directory rather than a shared Confluent API key. An organisation that signs in to Confluent Cloud through Microsoft Entra ID can point Kpow at the same directory over SAML or OpenID Connect, so one set of directory groups decides who reaches the console and who reaches Kpow, and a leaver is removed from both in one place.

Running managed connectors. Kpow reaches Confluent Managed Connect through the Confluent Cloud Connect API for the environment and cluster, with a cloud API key, so connector state and configuration sit next to the topics they read and write.

Reading topics against Schema Registry. Kpow connects to Confluent Schema Registry with an API key, mTLS or OAuth, so data inspect shows records decoded, and engineers filter them across topics with kJQ. Where schemas carry data contracts, data inspect marks records that failed a CEL, CEL_FIELD or JSONata rule, and kJQ filters on that result.

Working with ksqlDB. Kpow Enterprise connects to Confluent Cloud’s hosted ksqlDB over TLS with ALPN and a ksqlDB API key, so streams, tables and queries are in the same tool as the topics behind them.

Watching disk, lag and brokers. With a cloud API key, Kpow reads retained bytes and active connections from the Confluent Metrics API, and on very large clusters CONFLUENT_DISK_MODE=INFERRED keeps it within the API’s 50 requests a minute. Kpow’s Confluent Cloud documentation gives the arithmetic: the Metrics API returns at most 1,000 results for each query, Kpow sends 8 requests in parallel by default, and a cluster with more than 50,000 partitions exceeds the limit when disk is queried for every partition. The limit is counted for each IP address, so a Prometheus or Grafana scraper that leaves through the same NAT address draws on the same 50 requests, which is worth checking before blaming either tool for gaps in disk metrics. Kpow shows each consumer group down to partition level and resets, clears or skips offsets from the same view, and exports lag and the Confluent Cloud metrics to Prometheus.

Giving teams their own view of a shared cluster. Tenants limit which topics, groups and connectors each role can see, RBAC sets what each person may do, staged mutations hold an offset reset or topic deletion for approval, temporary policies grant production access that expires at a set time, and data policies mask sensitive fields in data inspect results on the server. A temporary policy lasts at most seven days unless the limit is changed, can be given a custom expiry date and time, and is written to the audit log. Roles, tenants and policies live in one YAML file tied to identity provider groups, which gives an auditor what an access review asks for, the role definitions, the role assignments and a trail of actions, in place of a list of role bindings on a shared service account.

Keeping the record. The audit log records each action with the user from the identity provider, which is the per-person record Confluent’s own audit log cannot give for a shared tool, and a webhook sends those records to Slack, Microsoft Teams or any endpoint. The webhook can send mutations, data inspect queries or both, so reads are recorded as well as changes. The audit log is a topic in the team’s own Confluent Cloud cluster, and the Kpow UI shows the last seven days of it, the same default window as Confluent’s audit log cluster, so a team with a longer retention requirement should send both records to its SIEM from the first day.

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FAQ

What is the best Kafka UI for Confluent Cloud?

On this page’s rubric, Kpow, with 89 of 100 points: it connects with API keys, OAuth or mTLS, manages fully managed connectors, reads Schema Registry and its data contract rules, works with hosted ksqlDB, adds per-person roles, masking, approvals and an audit trail on top of Confluent RBAC, and runs as one container in your own network outside the data path. The Confluent Cloud Console is second with nothing to deploy, and AKHQ is the highest-scoring open-source option.

Do I need a Kafka UI if I already have the Confluent Cloud Console?

Not for a small team where every engineer can hold their own Confluent role bindings. A separate tool earns its place when many engineers share production clusters and need masking, approvals, access that expires and a per-person audit trail, or when Confluent Cloud sits beside Confluent Platform, MSK or self-managed Kafka and the team wants one tool across all of them.

Can Confluent Control Center manage Confluent Cloud clusters?

Not as a management UI. Confluent documents connecting Control Center (Legacy) to Confluent Cloud to monitor data streams through client interceptors, which needs an additional subscription unless the organisation has committed usage. On Confluent Cloud the management UI is the Confluent Cloud Console, and Confluent’s Unified Stream Manager brings registered self-managed Confluent Platform clusters into that console. The comparison with a third-party tool is in Kpow vs Confluent Control Center.

Does IBM’s acquisition of Confluent change which Kafka UI to use on Confluent Cloud?

IBM completed its acquisition of Confluent on 17 March 2026. The Confluent Cloud Console stays the native UI for Confluent Cloud. A team that wants the same operating tool whether its clusters run on Confluent Cloud, Confluent Platform, Amazon MSK, WarpStream or self-managed Apache Kafka can run Kpow, whose documentation covers each of those providers from one deployment. What the deal means for Kafka users more broadly is covered in what the IBM-Confluent deal means for Kafka users.

How do I search a whole Confluent Cloud topic for one record?

The Confluent Cloud Console’s message browser filters only the results it has loaded, and its Advanced Message Search, which searches a topic’s full retained history, is in Open Preview, which Confluent offers for evaluation and non-production testing. Kpow’s data inspect searches across one or more topics and filters records with kJQ as it reads them, decoded against Schema Registry. Other options are compared in Kafka message search tools.

Which Kafka UI shows records that failed a Confluent data contract rule?

Kpow’s data inspect identifies records that failed a CEL, CEL_FIELD or JSONata data rule defined in Confluent Schema Registry and lets engineers filter them with kJQ, added in release 95.1. The Confluent Cloud Console’s message browser validates a message against its schema before producing it.

Which Kafka UI can manage Confluent Cloud’s fully managed connectors?

Kpow documents Confluent Managed Connect through the Confluent Cloud Connect API with a cloud API key. The Confluent Cloud Console manages them natively. The other tools on this page connect to Kafka Connect clusters by URL and do not describe Confluent’s managed connectors.

Does a Kafka UI need to sit in the data path to govern access on Confluent Cloud?

No. Kpow runs as one container in your own network, connects to Confluent Cloud like any Kafka client and applies roles, masking, approvals and the audit trail to the people working through it, so producers and consumers keep connecting straight to the brokers. Conduktor Console also connects directly, while Conduktor’s data-level controls run in Gateway, a Kafka proxy that client applications connect through.

Why does a shared Kafka tool need its own audit log on Confluent Cloud?

Confluent’s audit logs record each permission check with the principal that connected. A tool that many engineers share connects with one API key or service account, so Confluent records that service account. Kpow’s audit log records each action with the person from your identity provider.

Does the Confluent Cloud Console show topic data on a cluster with private networking?

Not from outside the private network. Confluent’s documentation states that on clusters with private networking the endpoints used for topic management and ksqlDB query management are not publicly accessible, so the console on a workstation outside the network cannot view messages. Confluent’s options are a resource metadata setting that shows topics and metrics without message data, or a proxy, reverse SSH tunnel or DNS configuration into the network, which it places outside its support scope. A self-hosted tool such as Kpow runs inside the VPC or VNet that holds the private endpoints and connects as an ordinary Kafka client.

Do Confluent Cloud audit logs show which engineer read a topic?

Not when engineers work through a shared tool. Confluent’s audit logs record the principal that connected, by ID, and for a shared tool that principal is the tool’s service account, so the engineer is not in the record. The logs sit on a separate read-only cluster that keeps seven days by default. Kpow’s audit log records data inspect queries with the user from the identity provider, and its webhook can send queries, mutations or both to a SIEM.

Should a Kafka UI connect to Confluent Cloud with a user’s API key or a service account’s?

A shared tool should use a key owned by a service account. Confluent’s documentation says permissions belong to the account that owns an API key and not to the key, that a key owned by a user account is deleted with that account, and that production workloads should use service account keys rotated every 90 days. A tool that covers Kafka, Schema Registry, ksqlDB, managed connectors and the Metrics API holds a resource-scoped key for each of the first three and a Cloud API key for the last two, so each needs a place in the rotation.

Can a Kafka UI show Confluent Cloud disk usage?

Confluent Cloud does not return disk usage through the Kafka AdminClient, so a tool has to read it from the Metrics API with a separate cloud API key. Kpow does, and on clusters too large for the API’s 50 requests a minute it can estimate partition-level disk use with CONFLUENT_DISK_MODE=INFERRED.

Is there a free Kafka UI for Confluent Cloud?

Kpow Community Edition is free on up to 3 clusters and 10 users and connects to Confluent Cloud, Managed Connect and Schema Registry. AKHQ and Kafbat UI are open source, and the Confluent Cloud Console is included with Confluent Cloud. RBAC, masking, staged approvals, the audit log and ksqlDB need Kpow Enterprise. More free options are compared in the best free Kafka UI tools.

How these tools were scored

Four of the six criteria start from Confluent’s Confluent Cloud documentation; the other two are where the tool runs and whether one deployment covers Confluent Cloud beside other clusters. They are listed here in order of weight. Each criterion is scored 0 to 10: 10 where a tool is the only one here doing it or clearly the best, 8 for a clean documented pass, 5 or 6 for partial support or support that needs work the reader must verify, 1 to 4 for a weak or indirect form, and 0 where it is absent. Where a tool was already scored on the same criterion and evidence on another Factor House page, it keeps that score here.

1. Governance beyond Confluent RBAC (counts three times). Confluent RBAC decides what a principal may do, and Confluent’s audit logs, available on Standard, Enterprise, Dedicated and Freight clusters and kept for seven days by default, record each permission check with that principal. Neither masks a field for one viewer, holds a topic deletion for approval or grants access that expires, and a shared tool’s API key makes the tool the principal. This criterion scores per-person roles, production access granted on request and expiring, masking, directory sign-in, tenants for teams sharing a cluster, and an audit trail that names the person. It is the same criterion as governance beyond IAM on the Amazon MSK page, so third-party tools keep their scores. The masking options are compared in Kafka data masking tools.

2. Out of the data path (counts twice). The tool should run inside your own network, reach the brokers as an ordinary Kafka client over the same endpoints your applications use, and keep no data outside your own cluster. Scored lower: tools that need an external database of their own, and tools whose controls work only when application traffic passes through a vendor’s proxy. A self-hosted container with no external database and no proxy scores 9, a tool with a database of its own 6, one with several databases or a component on the brokers 4, and one that needs both a database and a proxy for its controls 3; 10 is kept for an option with nothing to deploy at all, here the Confluent Cloud Console. This criterion is scored the same way on every Factor House page that uses it, and only its weight changes with the reader.

3. Connect, registry and ksqlDB (counts twice). Confluent Cloud’s fully managed connectors are run through the Confluent Cloud Connect API, Schema Registry and hosted ksqlDB each take their own API key, schemas can carry data contract rules, and disk usage comes only from the Metrics API. A tool that speaks only the Kafka Connect REST API of a self-hosted worker leaves the managed connectors out. A tool scores 9 or 10 here only when it documents managed connectors, Schema Registry and ksqlDB on Confluent Cloud; 7 when it adds Confluent Cloud API management to Schema Registry, self-hosted Connect and ksqlDB; 6 when it connects to Schema Registry, self-hosted Connect and ksqlDB; and 5 when it connects to Schema Registry and self-hosted Connect only. Confluent recommends its Flink service for new stream processing and keeps ksqlDB fully supported for existing applications, so ksqlDB is one part of this criterion rather than a criterion of its own.

4. API keys, OAuth and mTLS (counts once). A cluster API key over SASL/PLAIN is the common case, and every tool here can use one. OAuth through an identity pool needs Confluent’s logical cluster and identity pool extensions, and mTLS needs a client certificate signed by a CA uploaded to the organisation. A tool scores well when it documents all three, and lower when its own releases have broken the connection.

5. On-prem and cloud together (counts once). Many Confluent Cloud customers also run Confluent Platform, MSK or self-managed Kafka, as TD does. This criterion scores whether one deployment covers Confluent Cloud beside those clusters, and it uses the same scores as the banking page.

6. Inspecting topic data (counts once). Reading a message, searching a topic for one key or checking which records broke a data contract rule is the day-to-day job engineers need a tool for. The scores match the Amazon MSK page, with the Confluent Cloud Console scored on its message browser.

Costs are modelled for one production Confluent Cloud cluster and 25 engineers at $120 per engineer hour, using the same hours per tool class as Factor House’s other comparison pages. Tools with a licence carry the published price plus 2 hours a month to run. The open-source UIs carry 6 hours a month, $8,640 a year, to run, secure and keep current. The Confluent Cloud Console has no licence and nothing to run, and Confluent bills the data its message browser consumes and produces as cluster usage. Kpow’s $7,380 uses the price of $4,500 per cluster with 100 users included. Kpow Community Edition is free for 3 clusters and 10 users, so a 25-engineer team is on Enterprise.

The criteria map onto the Confluent Cloud features in the figure below.

F1 From Confluent Cloud feature to what a Kafka tool has to do

What Confluent Cloud does What the tool has to do

API keys Clients authenticate with a cluster API key and secret over SASL/PLAIN and TLS Hold a cluster API key and verify the broker hostname over TLS

OAuth and mTLS Workloads can federate through an identity pool, and supported clusters accept client certificates signed by a CA you upload Pass the logical cluster and identity pool IDs in its OAuth settings, or present a client certificate

Managed connectors Fully managed connectors are run and described through the Confluent Cloud Connect API Call that API with a cloud API key, not only a self-hosted Kafka Connect REST endpoint

Schema Registry and data contracts Schemas can carry data quality rules written in CEL or JSONata Decode records against the registry and show which ones failed a rule

Metrics API Disk and connection metrics come from the Metrics API, which takes a cloud API key and limits requests per IP Use a separate cloud API key and stay inside the rate limit on large clusters

RBAC and audit logs Role bindings and audit log records name the principal that connects When people share one tool, add per-person roles, masking and an audit trail, because the principal is the tool's own service account

Where the tool runs Applications reach the brokers over public endpoints or private networking Run in your own network as an ordinary Kafka client, not as a proxy that applications route through

Every option is scored from 0 to 10 on each criterion, from the evidence and sources this page cites, and the reason for each score is on its card. The criteria are weighted: Governance beyond Confluent RBAC counts three times, Out of the data path counts twice, Connect, registry and ksqlDB counts twice, API keys, OAuth and mTLS counts once, On-prem and cloud together counts once and Inspecting topic data counts once, for a total out of 100. Governance beyond Confluent RBAC counts three times because a tool that many engineers share connects to Confluent Cloud with one API key or service account, so Confluent's role bindings and audit logs see that service account rather than the person, and per-person roles, production access granted on request, masking, directory sign-in, tenants for shared clusters and an audit trail that names the engineer have to come from the tool. Out of the data path and Connect, registry and ksqlDB count twice: a tool that applications connect through becomes part of the path every producer and consumer depends on, while one that needs a database of its own is one more component to run and secure, and a tool that cannot reach Confluent's managed connectors, registry and ksqlDB leaves engineers switching back to the Confluent Cloud Console for half their work. Sign-in, on-prem and cloud together, and inspecting topic data count once, because most tools here pass them or the reader only needs them when the topology is mixed. This page is published by Factor House, which makes Kpow. Every option is scored on the same rubric and the same sources: Kpow's per-criterion scores are set the same way as every other option's and are not adjusted, and the weights apply to every option alike. Kpow ranks first on its total of 89 out of 100. The other options follow by total. Conduktor is listed last whatever its total; on its total of 70 it would place third.

Related reading

• Kafka: the complete guide
• Kafka-compatible cloud brokers
• Set Up Kpow with Confluent Cloud
• Running Kafka at bank scale
• Best Kafka UI tools for Amazon MSK
• Best Kafka management tools for banks
• Kpow vs Confluent Control Center
• Kafka SSO tools
• What the IBM-Confluent deal means for Kafka users
• The best Kafka management tools
• Kafka schema registry tools